Within-Subject Variability and Boosting of T-Cell Interferon-γ Responses after Tuberculin Skin Testing
Bibliographic record
Abstract
RATIONALE: The optimal strategy for the diagnosis of latent tuberculosis infection is controversial. Adoption of a two-step strategy (tuberculin skin test [TST] followed by an IFN-gamma release assay [IGRA], compared with an IGRA alone), may be limited by TST-mediated boosting of subsequent IGRA responses. Assessment of within-subject IGRA variability will aid in establishing thresholds for conversions and reversions, and interpretation of serial testing results. OBJECTIVES: To determine short-term IGRA variability and the impact of TST on subsequent IGRA results. METHODS: Within-subject variability and TST-mediated boosting of IGRA responses were evaluated in 26 South African participants with varying exposure risk. IGRAs (T-SPOT.TB, QuantiFERON-TB Gold In-Tube [QuantiFERON-TB-GIT], PPD, and heparin-binding hemagglutinin) were repeated four times over 21 days pre-TST, and on Days 3, 7, 28, and 84 post-TST administration. MEASUREMENTS AND MAIN RESULTS: All participants showed within-subject IGRA variability. Changes of +/-3 spots (T-SPOT.TB) or +/-80% from the mean IFN-gamma response (QuantiFERON-TB-GIT) over 3 weeks explained 95% of the variability. Spontaneous conversions/reversions occurred in 7 of 26 subjects (27%) (6 for T-SPOT.TB and 1 for QuantiFERON-TB-GIT [P = 0.049]) during the within-patient variability studies (pre-TST). After the TST eight subjects (33%) boosted above the defined baseline variability. By Day 7 post-TST, but not Day 3, 2 (12.5%) initially IGRA-negative test subjects converted. By contrast, boosting of PPD and heparin-binding hemagglutinin occurred by Day 3 post-TST. CONCLUSIONS: When using a two-step screening strategy it appears safe to perform a QuantiFERON-TB-GIT or T-SPOT.TB IGRA within 3 days of performing the TST. A 3-spot or 80% IFN-gamma response variation, on either side of baseline values, explains 95% of the short-term variability and may be useful for interpreting conversions and reversions, and values close to the cut-point.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".